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We present a general method, the &test, which establishes functional dependencies given a sequence of measurements. The approach is based on calculating conditional probabilities from vector component distances. Imposing the requirement of continuity of the underlying function, the obtained values of the conditional probabilities carry information on the(More)
We devise a feed-forward Artiicial Neural Network (ANN) procedure for predicting utility loads and present the resulting predictions for two test problems given by \The Great Energy Predictor Shootout-The First Building Data Analysis and Prediction Competition" 1]. Key ingredients in our approach are a method (test) for determining relevant inputs and the(More)
Self-organizing neural networks are brieey reviewed and compared with supervised learning algorithms like back-propagation. The power of self-organization networks is in their capability of displaying typical features in a transparent manner. This is successfully demonstrated with two applications from hadronic jet physics; hadronization model(More)
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